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                <div class="container"><article class="page"><h1 class="post-title animated flipInX">备战美赛</h1><div class="post-meta">
            <div class="post-meta-main"><a class="author" href="/" rel="author" target="_blank">
                    <i class="fas fa-user-circle fa-fw"></i>ChenDong Zhu
                </a>&nbsp;<span class="post-category">收录于&nbsp;<i class="far fa-folder fa-fw"></i><a href="/categories/%E6%AF%94%E8%B5%9B/">比赛</a>&nbsp;</span></div>
            <div class="post-meta-other"><i class="far fa-calendar-alt fa-fw"></i><time datetime=2020-12-31>2020-12-31</time>&nbsp;
                <i class="fas fa-pencil-alt fa-fw"></i>约 1294 字&nbsp;
                <i class="far fa-clock fa-fw"></i>预计阅读 3 分钟&nbsp;</div>
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                <h2 class="post-toc-title">目录</h2>
                <div class="post-toc-content"><nav id="TableOfContents">
  <ul>
    <li><a href="#备战美赛">备战美赛</a>
      <ul>
        <li><a href="#2020-problem-c--a-wealthy-of-data">2020 Problem C : A Wealthy of Data</a>
          <ul>
            <li><a href="#题目概述">题目概述</a></li>
            <li><a href="#2020总览">2020总览</a></li>
            <li><a href="#paper-from-2003717">Paper From 2003717</a></li>
          </ul>
        </li>
        <li><a href="#paper-from-2002116">Paper From 2002116</a></li>
        <li><a href="#paper-from-2009116">Paper From 2009116</a></li>
      </ul>
    </li>
  </ul>
</nav></div>
            </div>
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                <details>
                    <summary>
                        <div class="post-toc-title">
                            <span>目录</span>
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                    <div class="post-toc-content"><nav id="TableOfContentsMobile">
  <ul>
    <li><a href="#备战美赛">备战美赛</a>
      <ul>
        <li><a href="#2020-problem-c--a-wealthy-of-data">2020 Problem C : A Wealthy of Data</a>
          <ul>
            <li><a href="#题目概述">题目概述</a></li>
            <li><a href="#2020总览">2020总览</a></li>
            <li><a href="#paper-from-2003717">Paper From 2003717</a></li>
          </ul>
        </li>
        <li><a href="#paper-from-2002116">Paper From 2002116</a></li>
        <li><a href="#paper-from-2009116">Paper From 2009116</a></li>
      </ul>
    </li>
  </ul>
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                </details>
            </div><div class="post-content"><a class="post-dummy-target" id="备战美赛"></a><h2>备战美赛</h2>
<p>本身自己已经大四了，这次美赛是抱着“玩”的心态，比前几次少了一点对拿奖的渴望。同时这一次应该也是最有希望冲更高奖的一次，和数院统计专业的两位女生组了队，还被学弟戏称“老学长老学姐组”，由于距离比赛时间较长，我有比较充足的时间进行备赛，打算把前几年O奖的论文看一遍。以做研究读论文的方式，学习人家的长处。希望最后可以取得好成绩吧。因为是和两位统计专业的同学组了队，所以选题必然是大数据题，所以只看大数据。</p>
<a class="post-dummy-target" id="2020-problem-c--a-wealthy-of-data"></a><h3>2020 Problem C : A Wealthy of Data</h3>
<a class="post-dummy-target" id="题目概述"></a><h4>题目概述</h4>
<p>Amazon对一个网上产品，有<strong>Star Rating</strong>、<strong>Review</strong>、<strong>Helpfulness Rating</strong> 这三个评价因素。Star Rating 是购买者对产品的打分，有1到5颗星，Review是购买者对产品的文字评价，Helpfulness Rating是其他人对购买者的评论的打分，度量评论者的打分对自己的帮助程度，也是1到5颗星。</p>
<p>题目的问题有：</p>
<ol>
<li>
<p>上诉三个评价因素之间定性或定量的分析来分析三种产品</p>
</li>
<li>
<p>本题分a,b,c,d,e五个小题</p>
<p>a. 确定一个数字度量来帮助 SunShine 公司跟踪网上的产品
b. 定义和探讨一个和时间相关的度量来显示产品的名声是否在增长/下降
c. 定义一个结合了文本度量和星级度量的度量方式来显示一个产品成功和失败的可能性
d. 研究Review与Star Rating的关系
e. 研究Review中特定词对于Star Rating的关系</p>
</li>
</ol>
<a class="post-dummy-target" id="2020总览"></a><h4>2020总览</h4>
<table>
<thead>
<tr>
<th>队伍编号</th>
<th>思路</th>
<th>优点</th>
</tr>
</thead>
<tbody>
<tr>
<td>2003717</td>
<td>大量采用画图和观察画图结果来得出结论，使用了LSTM神经网络和时间序列分析。其他的公式很多都是来自于其他已发表论文的结论。</td>
<td>1. 关键字加粗<br/>2. 使用了Workflow指明了数据的来龙去脉<br/>3.专业的算法表述<br/>4.广搜论文，直接使用其他论文中的材料<br/>5.精确到word的情感分析<br/>6.summary写的不错，整篇文章逻辑性强</td>
</tr>
<tr>
<td>2002116</td>
<td>运用了大量的其他论文的公式，使用了某种情感分析的模型作为了文本的模型，并有该模型计算了review的信息量的多少，让公司跟踪少数的评论。总体上不如2003717。</td>
<td>1. 先提出了一些假设<br/>2. 模型的结构图</td>
</tr>
</tbody>
</table>
<a class="post-dummy-target" id="paper-from-2003717"></a><h4>Paper From 2003717</h4>
<ul>
<li>
<p>结论1：更高的Helpfulness Rate常伴随高Rating和长评论</p>
</li>
<li>
<p>结论2：早期每条评论的的平均Helpful votes远超过后期，同时相较于后期很多因素会剧烈变化（Rating等）</p>
</li>
<li>
<p>a小问的数字度量:</p>
<ul>
<li>weighted rating ratio</li>
<li>weighted sentimental score for reviews</li>
<li>preference vector</li>
</ul>
</li>
<li>
<p>b小问：产品名声与averaged star ratings, the authoritativeness of review number 相关</p>
</li>
<li>
<p>c小问：使用了b问中的Reputation，使用LSTM来对其进行预测</p>
</li>
<li>
<p>d小问结论：</p>
<ul>
<li>
<p>5星好评会吸引更多评论</p>
</li>
<li>
<p>1星差评与评论长度有强相关</p>
</li>
<li>
<p>使用了格兰杰因果关系验证（Granger Cause Test）得出：大量低星后会有短评论</p>
</li>
</ul>
</li>
<li>
<p>e小问：当同时出现positive和negative的词时，描述产品特征的词会代替有strongly affective words</p>
</li>
<li>
<p>具体解题思路：</p>
<ul>
<li>第一题定性分析，没有用到大量数学或统计学知识，靠作图，从图直观得到各类信息</li>
<li>第二题定量分析，采用其他论文的结论，配合LSTM进行预测和使用时间序列分析。</li>
</ul>
</li>
</ul>
<a class="post-dummy-target" id="paper-from-2002116"></a><h3>Paper From 2002116</h3>
<ul>
<li>研究重心似乎在价格策略，通过研究comment和rating来调整sale</li>
<li>使用了大量其他论文的公式</li>
<li>图画的不错，大量图可以借鉴</li>
<li>有把注意力放在每条评论的keyword</li>
</ul>
<a class="post-dummy-target" id="paper-from-2009116"></a><h3>Paper From 2009116</h3>
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